Leach Stephanie C, Hollow Hannah, Jiang Jiefeng, Hwang Kai
Psychological and Brain Sciences, The University of Iowa, Iowa City, IA.
Cognitive Control Collaborative (CCC).
bioRxiv. 2025 Aug 26:2025.03.21.644222. doi: 10.1101/2025.03.21.644222.
Adaptive behavior requires integrating information from multiple sources. These sources can originate from distinct channels, such as internally maintained latent cognitive representations or externally presented sensory cues. Because these signals are often stochastic and carry inherent uncertainty, integration is challenging. However, the neural and computational mechanisms that support the integration of such stochastic information remain unknown. We introduce a computational neuroimaging framework to elucidate how brain systems integrate internally maintained and externally cued stochastic information to guide behavior. Neuroimaging data were collected from healthy adult human participants (both male and female). Our computational model estimates trial-by-trial beliefs about internally maintained latent states and externally presented perceptual cues, then integrates them into a unified joint probability distribution. The entropy of this joint distribution quantifies overall uncertainty, which enables continuous tracking of probabilistic task beliefs, prediction errors, and updating dynamics. Results showed that latent state beliefs are encoded in distinct regions from perceptual beliefs. Latent-state beliefs were encoded in the anterior middle frontal gyrus, mediodorsal thalamus, and inferior parietal lobule, whereas perceptual beliefs were encoded in spatially distinct regions including lateral temporo-occipital areas, intraparietal sulcus, and precentral sulcus. The integrated joint probability and its entropy converged in frontoparietal hub areas, notably middle frontal gyrus and intraparietal sulcus. Entropy adaptively reconfigured connectivity-first strengthening coupling with sensory regions, then motor and premotor regions that encode task output, and finally prediction error circuits. These findings suggest that frontoparietal hubs read out and resolve distributed uncertainty to flexibly guide behavior, revealing how frontoparietal systems implement cognitive integration.
适应性行为需要整合来自多个来源的信息。这些来源可以源自不同的渠道，例如内部维持的潜在认知表征或外部呈现的感官线索。由于这些信号通常是随机的且带有内在的不确定性，整合具有挑战性。然而，支持整合此类随机信息的神经和计算机制仍然未知。我们引入了一个计算神经成像框架，以阐明大脑系统如何整合内部维持的和外部提示的随机信息来指导行为。神经成像数据是从健康的成年人类参与者（包括男性和女性）中收集的。我们的计算模型逐次试验地估计关于内部维持的潜在状态和外部呈现的感知线索的信念，然后将它们整合到一个统一的联合概率分布中。这个联合分布的熵量化了总体不确定性，这使得能够持续跟踪概率性任务信念、预测误差和更新动态。结果表明，潜在状态信念在与感知信念不同的区域中编码。潜在状态信念在前额中回、背内侧丘脑和顶下小叶中编码，而感知信念在空间上不同的区域中编码，包括颞枕外侧区域、顶内沟和中央前沟。整合后的联合概率及其熵在前额叶顶叶枢纽区域收敛，特别是额中回和顶内沟。熵自适应地重新配置连接性——首先加强与感觉区域的耦合，然后是编码任务输出的运动和运动前区域，最后是预测误差回路。这些发现表明，前额叶顶叶枢纽读出并解决分布式不确定性以灵活地指导行为，揭示了前额叶顶叶系统如何实现认知整合。
